Description: Data range normalization is a fundamental process in data preprocessing that involves adjusting the range of values in a dataset to fit within a specific interval, typically between 0 and 1 or -1 and 1. This procedure is crucial in data analysis and machine learning model building, as it allows different features or variables to have a comparable scale. Without normalization, variables with wider ranges can dominate the analysis, leading to biased or inaccurate results. Normalization helps improve the convergence of optimization algorithms and reduces the training time of models. There are several techniques for normalization, such as min-max normalization, which adjusts values based on the minimum and maximum of the dataset, and Z-score normalization, which uses the mean and standard deviation of the dataset. In summary, data range normalization is an essential technique that ensures data is treated fairly and effectively in analysis and modeling.
